Data Cleansing Tools

Our data cleansing tools and the data cleansing techniques we employ, enable us to manipulate your data file to help improve your overall data quality. This can include modifying the data format or data structure.

Even seemingly minor data format issues can present obstacles when processing your data. Luckily, our data cleansing team usually have an answer, and are happy to provide you with a free data quality assessment.

Regardless of how your data is stored, a large CRM database perhaps, or as an excel file maybe, the structure and data format are decisive factors in your ability to use it to its full potential.

Data scrubbing, data cleansing, whatever you decide to call it produce the same result – better data. However, success depends on using the correct data cleaning tools; otherwise, results are likely to be average at best and could ultimately cost more to correct in the future.

data cleaning tools that enhance data with services like dedupe and address correction
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What Types of Data Cleaning Tools?

Data cleansing tools are wide and varied, and dependent on what data cleaning task you need to complete there are some great ones out there.

For instance, Excel data cleansing tools and Salesforce data cleansing tools are really useful for general data cleaning and data manipulation.

If though, you have a large dataset that needs complex processing, say, deduping with hierarchy, then our professional data cleaning tools are a far better option.

Our data cleansing experience

The data cleansing methods we use are guaranteed to improve your data. As a result, a cluttered and messy file is transformed into a precise and structured format.

After our data refining processes are complete your database will be far more efficient. For instance, reporting will be significantly more insightful because all of your data will be utilised properly.

Optimised data will also allow you to drill down into each section, and hone your marketing campaigns far more effectively.

Data Cleansing
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Data Cleaning Examples

Below is a brief overview of data format issues that crop up on a regular basis when we cleanse name and address data. A more detailed data cleansing guide will be available soon which will showcase our full range of data cleaning tools.

If you want to be the first to receive it drop us an email here. Alternatively, use our contact form.


Table 1 has 5 sample records. Each has a combined name field, addr1-addr5, postcode, and an email address.

Table 1 Data Cleansing Examples - Messy data
IDNameAddr1Addr2Addr3Addr4Addr5PostcodeEmail
1Mr John Smith1 The StreetLEEDW. YorksL12345johnsmith@example.com
2Mr Peter Pan2 The StreetAny Addr2Any Addr3LondonE1 2SAp.pan@anywhere.com
3Mrs Jane Doe3 THE STREETAny Addr3Mancs M4 1ES jane.d@bloggs.com
4Miss Sam Sample4 The StreetNULLNULLLondonW11 3DBsamantha.samp@thesky.uk.co.
5Peter Pan2 The StANY ADDR2LondonE1 2SA

The name appearing in a single field isn’t a problem. However, the optimal way for this data to be stored within your database is by splitting it into three separate fields – Title, First name, and Surname.

This gives you another option too. You will have the option to create a custom salutation, so you could address the letter formally, or if you prefer, address them by their first name.

Now let’s look at the address elements which vary somewhat in data quality.

Address elements

Some records have missing address lines or postcodes and there seems to be little or no symmetry whatsoever.

Information is in the wrong field, with parts of the address incomplete or duplicated, while some records are clearly formatted incorrectly.

Incorrect email addresses also need to be rectified. Fortunately, our email data cleaning tool is perfect for this.

ID 4 shows Miss Sam Sample with ‘NULL’ showing in two fields. Some CRM databases will automatically attribute this ‘NULL’ value to a blank field. Consequently, data can be exported like this and cause problems, in a mail merge address block for example.

Dedupe data options

In the original table ID 2 is a duplicate of ID 5. but which record should be kept? With our dedupe processing options you can choose.

In this simple example ID 2 is the better record as it has more address details. For instance, a title appears before his name and he has an email address. It is a more comprehensive record.

Now lets say, for example, you had multiple data sources and one was your ‘Master’ file, the other data consisting of prospect lists. It would be great if you could choose the record to keep. Good news! with our data cleansing tool we can make it happen.

When we find a duplicate match, priority or hierarchy can be given to your master record and the prospect version removed from your mailing file or flagged for your database, if your prefer.

no more manual data correction

Now lets take a look at how your amended table could look after being run through our data cleaning software.

Table 2 Example Data Cleansed
TitleFirstLastIDNameAddr1Addr2Addr3TownCountyPostcodeEmail
MrJohnSmith1Mr John Smith1 The StreetAny Addr2Any Addr3LeedsWest YorkshireL12 345johnsmith@example.com
MrPeterPan2Mr Peter Pan2 The StreetAny Addr2Any Addr3LondonE1 2SAp.pan@anywhere.com
MrsJaneDoe3Mrs Jane Doe3 The StreetAny Addr2Any Addr3ManchesterGt. ManchesterM4 1ESjane.d@bloggs.com
MissSamSample4Miss Sam Sample4 The StreetAny Addr2Any Addr3
London
W11 3DBsamantha.samp@thesky.co.uk

The data has been corrected and tidied up and is far more usable. This is obviously a small snapshot of what can be achieved.

If you need advanced data cleaning tools for your next data project, we can help.

Use one of our contact options below to get in touch.